Environmental filters reduce the effects of sampling bias and improve predictions of ecological niche models

TLDR

Ecological niche models are valuable tools in biogeography, yet their utility is limited by sampling bias. The study investigates whether applying geographic or environmental filtering to calibration data can mitigate sampling bias. Using a virtual species projected onto the Iberian Peninsula, the authors sampled biased distributions, built models of varying sample sizes, and applied each filtering approach. Environmental filtering consistently improved model discrimination, with few climatically filtered points outperforming many unfiltered biased points, while geographic filtering offered no benefit.

Abstract

Ecological niche models represent key tools in biogeography but the effects of biased sampling hinder their use. Here, we address the utility of two forms of filtering the calibration data set (geographic and environmental) to reduce the effects of sampling bias. To do so we created a virtual species, projected its niche to the Iberian Peninsula and took samples from its binary geographic distribution using several biases. We then built models for various sample sizes after applying each of the filtering approaches. While geographic filtering did not improve discriminatory ability (and sometimes worsened it), environmental filtering consistently led to better models. Models made with few but climatically filtered points performed better than those made with many unfiltered (biased) points. Future research should address additional factors such as the complexity of the species’ niche, strength of filtering, and ability to predict suitability (rather than focus purely on discrimination).